Academic Radiology
Volume 14, Issue 10 , Pages 1242-1251, October 2007

A Validation Framework for Brain Tumor Segmentation1

  • Neculai Archip, PhD

      Affiliations

    • Harvard Medical School, Brigham and Women’s Hospital, 75 Francis St, Boston, MA 02115
    • Corresponding Author InformationAddress correspondence to: C.R.W.
    • ,
  • Ferenc A. Jolesz, MD

      Affiliations

    • Harvard Medical School, Brigham and Women’s Hospital, 75 Francis St, Boston, MA 02115
    • ,
  • Simon K. Warfield, PhD

      Affiliations

    • Computational Radiology Laboratory, Children’s Hospital Boston, Harvard Medical School.

Received 13 October 2006; accepted 10 May 2007.

Rationale and Objectives

We introduce a validation framework for the segmentation of brain tumors from magnetic resonance (MR) images. A novel unsupervised semiautomatic brain tumor segmentation algorithm is also presented.

Materials and Methods

The proposed framework consists of 1) T1-weighted MR images of patients with brain tumors, 2) segmentation of brain tumors performed by four independent experts, 3) segmentation of brain tumors generated by a semiautomatic algorithm, and 4) a software tool that estimates the performance of segmentation algorithms.

Results

We demonstrate the validation of the novel segmentation algorithm within the proposed framework. We show its performance and compare it with existent segmentation. The image datasets and software are available at http://www.brain-tumor-repository.org/.

Conclusions

We present an Internet resource that provides access to MR brain tumor image data and segmentation that can be openly used by the research community. Its purpose is to encourage the development and evaluation of segmentation methods by providing raw test and image data, human expert segmentation results, and methods for comparing segmentation results.

Key Words: Brain tumor segmentation, imaging, repository, validation, STAPLE, spectral clustering

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1 This investigation was supported in part by NSF ITR 0426558, NMSS Award #RG 3478A2/2, “a research grant from CIMIT”, and by NIH grants R03 EB006515, U41 RR019703, P01 CA067165, R01 RR021885, R03 CA126466, P30 HD018655, R01 HL074942, NIHR01 GM074068.

PII: S1076-6332(07)00407-2

doi:10.1016/j.acra.2007.05.025

Academic Radiology
Volume 14, Issue 10 , Pages 1242-1251, October 2007

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